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Spatially-Explicit Capture Recapture Methods

Ecological Statistics

  1. Arjun M. Gopalaswamy1,2,3

Published Online: 15 JAN 2013

DOI: 10.1002/9780470057339.vnn139

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Gopalaswamy, A. M. 2013. Spatially-Explicit Capture Recapture Methods. Encyclopedia of Environmetrics. 1.

Author Information

  1. 1

    University of Oxford, Oxford, UK

  2. 2

    Centre for Wildlife Studies, Bengaluru, India

  3. 3

    Wildlife Conservation Society-India program, Bengaluru, India

Publication History

  1. Published Online: 15 JAN 2013


Animal density is a parameter of basic interest to field biologists. But owing to logistical and analytical constraints, estimating animal density has been very challenging. Spatially explicit capture–recapture (SECR) models were developed to address this particular, long-standing problem. In this article, some of the important “milestones” in the SECR literature are discussed. The basic idea in SECR models is to include information about locations of animal captures into the modeling so that density is estimated directly. The early SECR type of approach involved estimation of density via simulations and inverse prediction. The first direct SECR estimator was likelihood-based, which was more flexible and allowed for inclusion of covariates. In parallel began the development of Bayesian versions of the SECR models. These allowed for greater flexibility and for extensions which could, in addition, also estimate population vital rates. SECR models have witnessed several applications, ranging from analysis of camera-trap data on large cats to acoustic signal data from birds. The future of SECR models is expected to go far beyond density estimation. It has great potential to answer some long-standing questions about how behavioral processes drive population dynamics in wild animals.


  • density estimation;
  • population size;
  • data augmentation;
  • camera trapping;
  • elusive species;
  • hair snares;
  • search encounters;
  • likelihood models;
  • bayesian models;
  • MCMC implementation